Water quality prediction, a well-established field with broad implications across various sectors, is thoroughly examined in this comprehensive review. Through an exhaustive analysis of over 170 studies conducted in the last five years, we focus on the application of machine learning for predicting water quality. The review begins by presenting the latest methodologies for acquiring water quality data. Categorizing machine learning-based predictions for water quality into two primary segments—indicator prediction and water quality index prediction—further distinguishes between single-indicator and multi-indicator predictions. A meticulous examination of each method’s technical details follows. This article explores current cutting-edge research trends in machine learning algorithms, providing a technical perspective on their application in water quality prediction. It investigates the utilization of algorithms in predicting water quality and concludes by highlighting significant challenges and future research directions. Emphasis is placed on key areas such as hydrodynamic water quality coupling, effective data processing and acquisition, and mitigating model uncertainty. The paper provides a detailed perspective on the present state of application and the principal characteristics of emerging technologies in water quality prediction.

Background: Substantial studies have reported the prevalence and the affecting factors of subjective cognitive decline (SCD). The complaints screening scale has also been used for probing. However, little is known in China. Objective: To investigate the prevalence and risk factors of SCD, and explore an SCD complaints screening scale in China. Methods: Stratified cluster random sampling was conducted. 2,689 residents aged 60–80 years completed questionnaire 1. 814 residents were included for clinical and neuropsychological evaluations. Two standards were used to make the diagnosis of mild cognitive impairment (MCI) and SCD, and a preliminary screening rate comparison was carried out. Finally, we assessed the risk factors of SCD and the correlation between the SCD-questionnaire 9 (SCD-Q9) and the Auditory Verbal Learning Test-Long Delay Free Recall (AVLT-LR). Results: 1) Standard 1 (ADNI2): the prevalence of SCD was 18.8% (95% CI = 14.7–22.9%) and zero conformed to six criteria (SCD plus). 2) Standard 2 (Jak/Bondi): the prevalence of SCD was 14.4% (95% CI = 10.7–18.1%). 3) Standard 1 had a relatively higher “false” positive rate, whereas Standard 2 had higher “false” negative rate. 4) Age, low education, fewer close friends, and daily drinking were independent risk factors for SCD progressing to MCI. 5) Total points of SCD-Q9 were negatively correlated to the value of AVLT-LR. Conclusions: The prevalence of SCD is high in the ShunYi District in Beijing, China. Age, low education, less social support, and daily drinking are independent risk factors. The brief SCD-Q9 can be used as a reference.

Background: Plasma amyloid-β (Aβ) may facilitate identification of individuals with brain amyloidosis. Gut microbial dysbiosis in Alzheimer’s disease (AD) is increasingly being recognized. However, knowledge about alterations of gut microbiota in preclinical AD, as well as whether the combination of plasma Aβ and gut microbiota could identify preclinical AD, remains largely unknown. Methods: This study recruited 34 Aβ-negative cognitively normal (CN−) participants, 32 Aβ-positive cognitively normal (CN+) participants, and 22 patients with cognitive impairment (CI), including 11 patients with mild cognitive impairment (MCI) and 11 AD dementia patients. All participants underwent neuropsychological assessments and fecal microbiota analysis through 16S ribosomal RNA (rRNA) Illumina Miseq sequencing technique. Meso Scale Discovery (MSD) kits were used to quantify the plasma Aβ₄₀, Aβ₄₂, and Aβ₄₂/Aβ₄₀ in CN− and CN+ participants. Using Spearman’s correlation analysis, the associations of global standard uptake value rate (SUVR) with altered gut microbiota and plasma Aβ markers were separately evaluated. Furthermore, the discriminative power of the combination of gut microbiota and plasma Aβ markers for identifying CN+ individuals was investigated. Results: Compared with the CN− group, the CN+ group showed significantly reduced plasma Aβ₄₂ (p = 0.011) and Aβ₄₂/Aβ₄₀ (p = 0.003). The relative abundance of phylum Bacteroidetes was significantly enriched, whereas phylum Firmicutes and class Deltaproteobacteria were significantly decreased in CN+ individuals in comparison with that in CN− individuals. Particularly, the relative abundance of phylum Firmicutes and its corresponding SCFA-producing bacteria exhibited a progressive decline tendency from CN− to CN+ and CI. Besides, the global brain Aβ burden was negatively associated with the plasma Aβ₄₂/Aβ₄₀ (r = −0.298, p = 0.015), family Desulfovibrionaceae (r = −0.331, p = 0.007), genus Bilophila (r = −0.247, p = 0.046), and genus Faecalibacterium (r = −0.291, p = 0.018) for all CN participants. Finally, the combination of plasma Aβ markers, altered gut microbiota, and cognitive performance reached a relatively good discriminative power in identifying individuals with CN+ from CN− (AUC = 0.869, 95% CI 0.782 ~ 0.955). Conclusions: This study provided the evidence that the gut microbial composition was altered in preclinical AD. The combination of plasma Aβ and gut microbiota may serve as a non-invasive, cost-effective diagnostic tool for early AD screening. Targeting the gut microbiota may be a novel therapeutic strategy for AD. Trial registration: This study has been registered in ClinicalTrials.gov (NCT03370744, https://www.clinicaltrials.gov) in November 15, 2017.

Over the past decades, accumulating evidence suggests that the gut microbiome exerts a key role in Alzheimer's disease (AD). The Alzheimer's Association Workgroup is updating the diagnostic criteria for AD, which changed the profiles and categorization of biomarkers from “AT(N)” to “ATNIVS.” Previously, most of studies focus on the correlation between the gut microbiome and amyloid beta deposition (“A”), the initial AD pathological feature triggering the “downstream” tauopathy and neurodegeneration. However, limited research investigated the interactions between the gut microbiome and other AD pathogenesis (“TNIVS”). In this review, we summarize current findings of the gut microbial characteristics in the whole spectrum of AD. Then, we describe the association of the gut microbiome with updated biomarker categories of AD pathogenesis. In addition, we outline the gut microbiome‐related therapeutic strategies for AD. Finally, we discuss current key issues of the gut microbiome research in the AD field and future research directions.Highlights: The new revised criteria for Alzheimer's disease (AD) proposed by the Alzheimer's Association Workgroup have updated the profiles and categorization of biomarkers from “AT(N)” to “ATNIVS.” The associations of the gut microbiome with updated biomarker categories of AD pathogenesis are described. Current findings of the gut microbial characteristics in the whole spectrum of AD are summarized. Therapeutic strategies for AD based on the gut microbiome are proposed.